library(readr)
library(ggplot2)
library("tidyverse")
library("dplyr")
library("plotly")
- Importing the Data 1.1. Housing Prices Database
hpa <- read_csv("E:/DataScienceMBA/TCC/Database/HPA02.20220813T000818.csv", show_col_types = FALSE)
new_names <- c("Stat", "Year", "County","Dwelling", "Stamp", "Buyer", "Sale", "Unit", "Value")
names(hpa) <- new_names
head(hpa)
group_sale <- group_by(hpa, Sale, Year)
group_county <- group_by(hpa, County, Year)
group_stamp <- group_by(hpa, Stamp, Year)
group_dwelling <- group_by(hpa, Dwelling, Year)
by_sale <- group_sale %>%summarise( ave = mean(Value,na.rm = TRUE) ,med = median(Value,na.rm = TRUE), max = max(Value,na.rm = TRUE), min = min(Value,na.rm = TRUE))
`summarise()` has grouped output by 'Sale'. You can override using the `.groups` argument.
by_county <- group_county %>% summarise( ave = mean(Value,na.rm = TRUE) ,med = median(Value,na.rm = TRUE), max = max(Value,na.rm = TRUE), min = min(Value,na.rm = TRUE))
`summarise()` has grouped output by 'County'. You can override using the `.groups` argument.
group_stamp %>% summarise( ave = mean(Value,na.rm = TRUE) ,med = median(Value,na.rm = TRUE), max = max(Value,na.rm = TRUE), min = min(Value,na.rm = TRUE))
`summarise()` has grouped output by 'Stamp'. You can override using the `.groups` argument.
group_dwelling %>% summarise( ave = mean(Value,na.rm = TRUE) ,med = median(Value,na.rm = TRUE), max = max(Value,na.rm = TRUE), min = min(Value,na.rm = TRUE))
`summarise()` has grouped output by 'Dwelling'. You can override using the `.groups` argument.
interval <- 2010:2022
hpa %>% filter (Year %in% interval) %>%
ggplot(aes(x = factor(Year), y = Value, fill = factor(Sale))) + geom_boxplot(outlier.shape=NA) + facet_wrap(~Sale, nrow = 10) + xlab("Year")+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +ylim(0, 500000)

interval <- 2010:2022
hpa %>% filter (Year %in% interval) %>%
ggplot(aes(x = factor(Year), y = Value, fill = factor(Dwelling))) + geom_boxplot(outlier.shape=NA) + facet_wrap(~Dwelling, nrow = 10) + xlab("Year")+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +ylim(0, 500000)

interval <- 2010:2022
hpa %>% filter (Year %in% interval) %>%
ggplot(aes(x = factor(Year), y = Value, fill = factor(Stamp))) + geom_boxplot(outlier.shape=NA) + facet_wrap(~Stamp, nrow = 10) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +ylim(0, 500000)

interval <- 2010:2022
hpa %>% filter (Year %in% interval) %>%
ggplot(aes(x = factor(Year), y = Value, fill = factor(County))) + geom_boxplot(outlier.shape=NA) + facet_wrap(~County) + xlab("Year") +ylim(0, 500000)

interval <- 2010:2022
p<-by_county %>% filter (Year %in% interval) %>%
ggplot(aes(x =Year, y = med, group = County, color = factor(County))) + geom_line() +ylim(0, 450000) + ylab("Median of House Values")
plotly::ggplotly(p)
NA
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